Abstract
A novel method for learning a linear multilayer feedforward artificial neural network (ANN) by using ensembles of boosted decision stumps is presented. Network parameters are adapted through a layer-wise iterative traversal of neurons with weights of each neuron learned by using a boosting based ensemble and an appropriate reduction. Performances of several neural network models using the proposed method are compared for a variety of datasets with networks learned using three other algorithms, namely Perceptron learning rule, gradient decent back propagation algorithm, and Boostron learning.
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References
Baig, M.M., Awais, M.M., El-Alfy, E.-S.M.: BOOSTRON: boosting based perceptron learning. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part I. LNCS, vol. 8834, pp. 199–206. Springer, Heidelberg (2014)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2(1), 263–286 (1995)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Ann. Stat. 26(2), 451–471 (1998)
Iba, W., Langley, P.: Induction of one-level decision trees. In: Proceedings of the 9th International Conference on Machine Learning, pp. 233–240 (1992)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)
Rumelhart, D.E., McClelland, J.L., PDP Research Group.: Parallel Distributed Processing: Explorations in the Microstructures of Cognition, vol. 1. MIT Press, Cambridge, MA, USA (1986)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)
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Baig, M.M., El-Alfy, ES.M., Awais, M.M. (2015). Learning Rule for Linear Multilayer Feedforward ANN by Boosted Decision Stumps. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_38
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DOI: https://doi.org/10.1007/978-3-319-26532-2_38
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